segnext_mscan-t_1xb16-adamw-160k_ade20k-512x512.py

_base_ = [
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py',
    '../_base_/datasets/ade20k.py'
]
# model settings
checkpoint_file = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segnext/mscan_t_20230227-119e8c9f.pth'  # noqa
ham_norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
crop_size = (512, 512)
data_preprocessor = dict(
    type='SegDataPreProcessor',
    mean=[123.675, 116.28, 103.53],
    std=[58.395, 57.12, 57.375],
    bgr_to_rgb=True,
    pad_val=0,
    seg_pad_val=255,
    size=(512, 512),
    test_cfg=dict(size_divisor=32))
model = dict(
    type='EncoderDecoder',
    data_preprocessor=data_preprocessor,
    pretrained=None,
    backbone=dict(
        type='MSCAN',
        init_cfg=dict(type='Pretrained', checkpoint=checkpoint_file),
        embed_dims=[32, 64, 160, 256],
        mlp_ratios=[8, 8, 4, 4],
        drop_rate=0.0,
        drop_path_rate=0.1,
        depths=[3, 3, 5, 2],
        attention_kernel_sizes=[5, [1, 7], [1, 11], [1, 21]],
        attention_kernel_paddings=[2, [0, 3], [0, 5], [0, 10]],
        act_cfg=dict(type='GELU'),
        norm_cfg=dict(type='BN', requires_grad=True)),
    decode_head=dict(
        type='LightHamHead',
        in_channels=[64, 160, 256],
        in_index=[1, 2, 3],
        channels=256,
        ham_channels=256,
        dropout_ratio=0.1,
        num_classes=150,
        norm_cfg=ham_norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
        ham_kwargs=dict(
            MD_S=1,
            MD_R=16,
            train_steps=6,
            eval_steps=7,
            inv_t=100,
            rand_init=True)),
    # model training and testing settings
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))

# dataset settings
train_dataloader = dict(batch_size=16)

# optimizer
optim_wrapper = dict(
    _delete_=True,
    type='OptimWrapper',
    optimizer=dict(
        type='AdamW', lr=0.00006, betas=(0.9, 0.999), weight_decay=0.01),
    paramwise_cfg=dict(
        custom_keys={
            'pos_block': dict(decay_mult=0.),
            'norm': dict(decay_mult=0.),
            'head': dict(lr_mult=10.)
        }))

param_scheduler = [
    dict(
        type='LinearLR', start_factor=1e-6, by_epoch=False, begin=0, end=1500),
    dict(
        type='PolyLR',
        power=1.0,
        begin=1500,
        end=160000,
        eta_min=0.0,
        by_epoch=False,
    )
]